Executive Summary

It’s widely believed that the most successful entrepreneurs are young. Bill Gates, Steve Jobs, and Mark Zuckerberg were in their earlytwenties when they launched what would become world-changing companies. Do these famous cases reflect a generalizable pattern? In fact, the average age of entrepreneurs at the time they founded their companies is 42.But what about the most successful startups? Is it possible that companies started by younger entrepreneurs are particularly successful? Research shows that among the top 0.1%ofstartupsbased on growthintheir first five years, the founders started their companies, on average, whentheywere 45 years old.

It’s widely believed that the most successful entrepreneurs are young. Bill Gates, Steve Jobs, and Mark Zuckerberg were in their earlytwenties when they launched what would become world-changing companies. Do these famous cases reflect a generalizablepattern? VC and media accounts seem to suggest so. When we analyzed founders who have won TechCrunch awards over the last decade,
the average age at the time of founding was just 31
. Forthe peopleselected by Inc. magazine as the founders of the fastest-growing startups in 2015,
the average age at founding was only 29
. Consistent with these findings, Paul Graham,acofounder of Y Combinator,
once quipped
that “the cutoff in investors’ heads is 32… After 32, they start to be a little skeptical.” But is this view correct?

Debunking the Myth of the Young Entrepreneur

Our team analyzed the age of all business founders in the U.S. in recent years by leveraging confidential administrative data sets from the U.S. Census Bureau. We found that the average age of entrepreneurs at the time they founded their companies is 42. But the vast majority of these new businesses are likely small businesses with no intentions to grow large (for example, dry cleaners and restaurants). To focus on businesses that are closer in spirit to the prototypical high-tech startup, we used a variety of indicators: whether the firm was granted a patent, received VC investment, or operated in an industrythat employs a high fraction of STEM workers. We also focused on the location of the firm, in particular whether it was in an entrepreneurial hub such as Silicon Valley. In general, these finer-grained analyses do not modify the main conclusion: The average age of high-tech founders falls in the early forties.

The relationship between the instantaneous generation of flaked particles and micro-arc discharge is investigated in mass-production plasma etching equipment. To investigate the mechanism of such particle generation, we simultaneously detect particle generation from deposited films on a ground electrode and occurrence of micro-arc discharge under mass-production conditions. The results indicate that the deposited films are severely damaged and flake off as numerous particles when micro-arc discharge occurs. The rapid changes in floating potential on the films due to micro-arc discharge cause electric field stress, which works as an impulsive force. The particles are generated not from the melting of chamber parts by micro-arc discharge but from the flaking of deposited films by electric field stress acting as an impulsive force.

As organic reach has dwindled in Facebook’s main app, posts from brands have decreased and — at least theoretically — more room has been made for posts from friends and family in your News Feed. Handily for Facebook, it’s also a roundabout way of fighting back against the spread of fake news that’s proliferated on the platform.

So, it’s like anything — and people and pages have adapted, using Facebook’s standalone apps like Messenger and WhatsApp to spread that content directly to other users. Which is why Facebook is in the process of testing some new features to do something about that.

WhatsApp in a
Clarks Wallbeck Edge Brown Waterproof Leather Shoes dBenSU
said it’s launching a new feature that will starting labeling messages in the app that have been forwarded along to a user instead of composed fresh. “This extra context,” the post notes, “will help make one-on-one and group chats easier to follow. It also helps you determine if your friend or relative wrote the message they sent or if it originally came from someone else.”

According to
this report
out of India, the feature comes in the wake of Facebook-owned WhatsApp getting a notice from the Indian government last week directing the company to “take immediate measures to prevent misuse of its platform.”

Indeed, the problem of using social media platforms like WhatsApp in India to spread fake news and rumors has been so pervasive India that it’s led to rumors that have spawned mob violence. And Facebook even taking out ads
in Indian newspapers
in recent days to warn people about misuse of such platforms.

The social media giant, meanwhile, is taking a similar tack with Messenger.

As first noted by
Motherboard
, Facebook is testing a Messenger feature that will let a user who gets a random missive in the Messenger app know if it was sent from an account that was recently created. Moreover, it will use the phone number connected to the account to identify its origin.

The feature — which Facebook has acknowledged it’s playing with at the moment as part a “small test” — isn’t specifically about going after Russian actors. Nevertheless,
Motherboard
shared a screenshot showing the feature can identify a message having been sent by someone who “logged in using a phone number from Russia.”

The stakes certainly could not be higher for Facebook to get this right. The company, at a minimum, can’t let these things go on unchecked.

If you want to record information about the sources and cases in your project, you can do this by assigning the source or case to a classification, and then setting the attribute value for each attribute.

You can create attribute values when you are classifying a specific source or case. For example, you could open the properties for the case
Mary
and create attribute values (
age=22
and
occupation=teacher
).

When an attribute has a finite list of possible values, you can define these values when you create the attribute. For example, the attribute s
ector
may have
industry
,
agriculture
and
retail
as the possible attribute values.

What do you want to do?

Understand attribute values

When you create a new attribute, it has two system defined attribute values:

'Unassigned'—used to indicate that the value of this attribute has not been assigned yet. This is the default value for the attribute, unless you select another value for the default.

'Not Applicable'—used to indicate that the value is not applicable for the source or case that you are classifying.

You can change the labels used for 'Unassigned' and 'Not Applicable' attribute values in your project, if these labels do not suit your project.

When an attribute has a finite list of possible values, you can define the values when you create the attribute. For example, the attribute s
ector
may have
industry
,
agriculture
and
retail
as the possible attribute values. You can add the attribute values manually or import them from a text file.

Attribute values can also be added, as needed, when you assign sources or cases to a classification and set their attribute values. For example, you could open the properties for the case
Mary
and create attribute values (
age=22
and
occupation=teacher
).

Any attribute values you add must be compatible with the attribute's
data type
. For example, if the data type is 'text' then you can enter alphanumeric characters.

The values of text attributes can be reordered—for example, if you have an attribute with the values
High
,
Low
and
Medium
, you might want the values to be ordered
Low
,
Medium
and
High
— this can be useful when you:

Generate charts or reports—the attribute values will appear in the order you specified

Use Advanced Find or queries and want to find sources or cases with attribute values that are greater or less than a selected value